Search Results (100)

Marine aerosol particles influence the climate, and interactions between ocean waves and coral reefs may impact aerosol size distributions in remote locations, such as the Great Barrier Reef. However, quantifying these processes has proven to be challenging. We tested whether marine aerosol size distributions and concentrations differ across four zones: background air outside the lagoon, above the reef crest, within the lagoon, and near the beach of Heron Island, approximately 85 km offshore. Using a modified DJI Matrice 600 hexacopter equipped with a miniaturised optical particle counter and custom inline gas dryer, we measured aerosols from 165 to 3000 nm across 64 drone flights during 16 sampling events in November 2024. Aerosol concentrations showed substantial day-to-day temporal variability, while spatial differences among reef zones were generally minor; on certain days, the maximum difference between background and near-island measurements reached approximately 25%. K-means clustering identified four dominant air mass transport patterns, and Hybrid Single-Particle Lagrangian Integrated Trajectory model analysis indicated that upwind conditions had a strong influence on aerosol loading. Vertical profiles revealed limited variability within the lowest 100 m. Mixing layer height, air parcel travel speed, and water depth along the final 12 h of trajectories were key drivers of aerosol variability. These results demonstrate the potential of drone-based measurements for characterising marine aerosols and provide a foundation for improving climate model representations of natural aerosol processes. Full article

This study investigates the environmental impact of combined missile and drone attacks on Kyiv, the capital of Ukraine, with a focus on the release of particulate matter (PM) into the urban atmosphere. These military strikes frequently result in the destruction of residential and industrial infrastructure, as well as fires, leading to acute increases in ambient concentrations of fine particulate matter (PM2.5). Observational data were collected between 1 and 30 June 2025 using a distributed network of low-cost air quality monitoring stations aggregated by the SaveEcoBot platform. The optical particle counters, based on light scattering technology, enable real-time monitoring of airborne particulate fractions of PM2.5 along with meteorological parameters and gas pollutants. The study period included two significant attacks (10 and 17 June), during which the temporal and spatial dynamics of PM2.5 concentrations were analyzed in comparison to baseline levels observed under non-attack conditions. Raw concentrations of PM2.5 up to 241 μg/m³ were observed in the epicenters of air-strike-induced fires, while smog plumes covered half of the city area. Elevated PM2.5 concentrations were recorded during and for several hours following the attacks and corresponding air raid alerts. The findings show days of PM2.5 exceedances above the World Health Organization (WHO) daily threshold of 15 μg/m³. These results underscore the acute environmental and public health hazards posed by military assaults on urban centers. Furthermore, this research highlights the role of citizen-driven environmental monitoring as a valuable tool for both scientific documentation and potential evidentiary support in assessing the environmental impacts of warfare. Full article

A sensor network based on 10 stationary nodes distributed in Bari (Southern Italy) has been deployed for urban air quality (AQ) monitoring. The low-cost sensor systems have been installed in specific sites (e.g., buildings, offices, schools, streets, ports, and airports) to enhance environmental awareness of the citizens and to supplement the expensive official air-monitoring stations with cost-effective sensor nodes at high spatial and temporal resolution. Continuous measurements were performed by low-cost electrochemical gas sensors (CO, NO₂, O₃), optical particle counter (PM₁₀), and NDIR infrared sensor (CO₂), including micro-sensors for temperature and relative humidity. The sensors are operated to assess the performance during a campaign (July 2015–December 2017) of several months for citizen science in sustainable smart cities. Typical values of CO₂, measured by distributed nodes, varied from 312 to 494 ppm (2016), and from 371 to 527 ppm (2017), depending on seasonal micro-climate change and site-specific conditions. The results of the AQ-monitoring long-term campaign for selected sensor nodes are presented with a relative error of 26.2% (PM₁₀), 21.7% (O₃), 25.5% (NO₂), and 79.4% (CO). These interesting results suggest a partial compliance, excluding CO, with Data Quality Objectives (DQO) by the European Air Quality Directive (2008/50/EC) for Indicative (Informative) Measurements. Full article

Indoor sports facilities present unique challenges for air quality management due to high crowd densities and limited ventilation. This study investigated air quality in a municipal athletic facility in Komotini, Greece, focusing on concentrations of airborne particulate matter (PM_1.0, PM_2.5, PM₁₀), humidity, and temperature across spectator zones, under varying mask scenarios. Sensing devices were installed in the stands to collect high-frequency environmental data. The system, based on optical particle counters and cloud-enabled analytics, enabled real-time data capture and retrospective analysis. The main experiment investigated the impact of spectators wearing medical masks during two basketball games. The results show consistently elevated PM levels during games, often exceeding recommended international thresholds in the spectator area. Notably, the use of masks by spectators led to measurable reductions in PM_1.0 and PM_2.5 concentrations, because they seem to have limited the release of human-generated aerosols as well as the amount of movement among spectators, supporting their effectiveness in limiting fine particulate exposure in inadequately ventilated environments. Humidity emerged as a reliable indicator of occupancy and potential high-risk periods, making it a valuable parameter for real-time monitoring. The findings underscore the urgent need for improved ventilation strategies in small to medium-sized indoor sports facilities and support the deployment of low-cost sensor networks for actionable environmental health management. Full article

Low-cost sensors have demonstrated their advances in acquiring hyper-localized data compared to traditional, high-maintenance air quality monitoring stations. The study aims to leverage the mobility of participants equipped with low-cost wearable monitors (LWMs) by comparing their exposure to particulate matter (PM) across indoor-home, outdoor-walking, and hybrid-commuting micro-environments. The LWMs would be calibrated first through field co-location and the multiple linear regression models. The coefficient of determination (R²) of PM_1.0 and PM_2.5 increased to over 0.85 after calibration, along with the reduced root mean square error of 2.25 and 3.46 μg/m³, respectively. The 26-day PM data collection with geographic locations could identify individual exposure patterns, local source contributions, and hotspot maps. Commuting constituted a small fraction of daily time (4–8%) but contributed a disproportionate impact, accounting for 11% of individual PM exposure. Indoor-home PM_2.5 exposure varied significantly among the urban districts. Based on the PM_2.5 hotspot map, the elevated concentration was mainly concentrated in dense residential areas and historical industrial areas, as well as interchanges of major roads and the highway system. LWMs acting as non-regulatory instruments can complement monitoring stations to provide missing short-term and hyper-localized air pollution data. Future studies should integrate long-term monitoring and citizen science across seasons and geographical regions to address pollutant spatiotemporal variability for building and city sustainability. Full article

Despite significant progress in controlling outdoor air pollution in Mexico City over the past three decades, indoor air pollution remains largely unaddressed. This is particularly concerning because health authorities advise people to stay indoors when outdoor pollution exceeds safe limits, yet indoor concentrations can be higher. Two optical particle counters were deployed simultaneously indoors and outdoors in 38 homes across all municipalities in Mexico City. The average indoor 24 h PM_2.5 concentration was 24.5 µg m⁻³, while PM₁₀ concentration averaged 78.6 µg m⁻³ compared to outdoor averages of 20.5 µg m⁻³ and 72.0 µg m⁻³. The PM_2.5/PM₁₀ ratio was 0.3 both indoors and outdoors. Only 20% of the homes exhibited maximum outdoor PM_2.5 concentrations 3.6 times higher than indoor; in 18%, indoor and outdoor levels were similar (0.8–1.2); and 60% of homes recorded indoor maxima up to nine times the outdoor peaks. Elevated indoor PM_2.5 was primarily linked with cooking and, to a lesser extent, cleaning activities. Peaks in PM_2.5 persisted for 4–8 h before returning to baseline. Ensuring adequate indoor ventilation is critical to maintain indoor air quality below outdoor levels and comply with WHO guidelines, highlighting the need for targeted strategies to reduce indoor exposure in urban homes. Full article

The article presents the results of original research on determining the abrasive properties of composite materials with an epoxy resin matrix reinforced with basalt particles in the form of powder and pine chips from the post-production waste of wooden elements. There are many studies available in the literature on the modification of composite materials in terms of achieving the required strength properties, but there is little information available in the area of achieving specific functional properties of composite materials, e.g., abrasive properties. Three composite materials with different proportions of the material components were made. These materials were tested using standardized tests to determine their mechanical properties, and these properties were compared in relation to the matrix material (epoxy resin). In order to determine the abrasive properties, an original research stand was made, on which the composites were tested using counter-samples made of an aluminum alloy. The mass loss of samples and counter-samples after the friction test was measured and determined. Changes in the electrospindle supply current and rotational measurements were also made. The values measured and determined in the tests were used as indicators of the abrasiveness of composite materials. It was shown that both the loss of mass of the sample and counter-sample and the parameters of the electrospindle operation are good, convenient indicators of the abrasive properties of the tested materials. The obtained results were subjected to statistical analyses. Optical 3D scans of the surfaces of exemplary samples were presented. Full article

Particulate matter (PM) is a major health risk, particularly in indoor environments where air quality should be optimized and pollution reduced efficiently. While technical air purification systems can be costly and impractical, indoor plants offer a sustainable alternative. Using a novel methodology, four common indoor plants were evaluated for their potential to reduce PM_2.5. PM_2.5 was introduced via incense in a custom-designed test chamber with air circulating at 0.3 m/s. Air quality was continuously monitored with an AirGradient Open Air device (Model O-1PST), an optical particle counter. Statistical significance was confirmed by independent t-tests and ANOVA. Calcium chloride regulated relative humidity in the chamber. The plants Epipremnum aureum, Chlorophytum comosum, Nephrolepis exaltata, and Maranta leuconeura were assessed for their PM_2.5-binding capacity. Nephrolepis exaltata showed the highest reduction efficiency. Maranta leuconeura with its hemispherical leaf cells was tested for the first time and proved to trap particles within its leaf structure. It is ranked second and showed a stronger dependence on ambient PM_2.5 concentrations for reduction efficiency. Full article

Low-cost particulate matter sensors have seen increased use for monitoring at personal and local levels due to their affordability, ease of operation, and high time resolution. However, the quality of data reported by these sensors can be questionable, and a thorough evaluation of their performance is necessary. This study evaluated the particle sizing accuracy of several commonly used optical sensors, including the Alphasense optical particle counter (OPC), TSI DustTrak DRX aerosol monitor, Plantower PMS5003 sensor, and Sensirion SPS30 sensor, using laboratory-generated monodisperse particles. The OPC and DRX agreed partially with reference instruments and showed promise in detecting coarse-size particles. However, the PMS5003 and SPS30 did not correctly size fine and coarse particles. Furthermore, their reported mass distributions do not directly correspond to their number distribution. Despite these limitations, field measurements involving a dust storm period showed that the SPS30 correlated reasonably well with reference instruments for both PM_2.5 and PM₁₀, though the regression slopes differed significantly. These findings underscore the need for caution when interpreting data from low-cost optical sensors, particularly for coarse particles. Recommendations for improving the performance of these sensors are also provided. Full article

An automatic optimization procedure is proposed for some operational parameters of a Parallel-Plate Avalanche Counter with Optical Readout, a detector designed for heavy-ion tracking and imaging. Exploiting differentiable programming and automatic differentiation, we model the reconstruction of the position of impinging 5.5 MeV alpha particles for different detector configurations and build an optimization cycle that minimizes an objective function. We analyze the performance improvement using this method, exploring the potential of these techniques in detector design. Full article

A beta-ray attenuation monitor (BAM) is preferred as a reference instrument for monitoring particulate matter in the air due to its accuracy. However, BAM cannot be used in large numbers for spatial distribution monitoring because of its high investment cost. Thus, a mid-cost optical particle counter (OPC) is an alternative solution for widespread use. However, its long-term performance with respect to various monitoring environments should be taken into account. In this study, six mid-cost OPCs were used to measure PM_2.5 concentrations at an underground subway station and compared with a reference BAM over 15 months. OPCs were placed in the waiting space and platforms to compare PM_2.5 concentrations and determine PM_2.5/PM₁₀ ratios. The reference BAM was installed on the platform. Error analysis revealed a significant discrepancy, with normalized errors exceeding 30%, between the 1-h average PM_2.5 concentrations recorded by the BAM and OPCs at the same location. In contrast, the 24-h average PM_2.5 concentrations measured by the BAM and OPCs at the same location showed similar patterns, with stronger correlations (r² = 0.80–0.93) compared to the 1-h averages (r² = 0.63–0.83). The normalized errors for the 24-h averages ranged from 13.9% to 21.2%, depending on seasonal variations. These findings suggest that OPCs can effectively monitor 24-h average PM_2.5 concentrations in an underground subway station over a year without additional calibration, making them a cost-effective option. In addition, 1-h average PM_2.5 concentrations varied across different sampling spaces and were influenced by PM_2.5/PM₁₀ ratios. Hence, when measuring the 1-h average mass concentration of PM_2.5, it is essential to consider PM characteristics and seasons. Full article

In this study, a drone-based measurement system equipped with miniaturized optical and condensation particle counters was deployed to investigate the vertical distribution of particulate matter and meteorological variables up to 2.5 km in altitude. Measurements captured at various altitudes demonstrated notable vertical variations in particle concentration and significant correlations with meteorological factors, particularly relative humidity (RH). Near the surface, within a well-mixed boundary layer, particle concentrations remained stable despite RH changes, indicating both anthropogenic and natural influences. At higher altitudes, a clear positive relationship between RH and particle number concentration emerged, particularly for smaller particles, while temperature inversions and distinct wind patterns influenced aerosol dispersion. The unmanned aerial vehicle system’s robust performance, validated against standard meteorological tower data, underscores its potential for high-resolution atmospheric profiling. These insights are crucial for understanding particle behavior in diverse atmospheric layers and have implications for refining air quality monitoring and climate models. Future work should incorporate chemical analysis of aerosols to further expand these findings and assess their environmental impact. Full article

Low-cost air quality sensors are a promising supplement to current reference methods for air quality monitoring but can suffer from issues that affect their measurement quality. Interferences from environmental conditions such as temperature, humidity, cross-sensitivities with other gases and a low signal-to-noise ratio make them difficult to use in air quality monitoring without significant time investment in calibrating and correcting their output. Many studies have approached these problems utilising a variety of techniques to correct for these biases. Some use physical methods, removing the variability in environmental conditions, whereas most adopt software corrections. However, these approaches are often not standardised, varying in study duration, measurement frequency, averaging period, average concentration of the target pollutant and the biases that are corrected. Some go further and include features with no direct connection to the measurement such as the level of traffic nearby, converting the initial measurement into a modelled value. Though overall trends in performance can be derived when aggregating the results from multiple studies, they do not always match observations from individual studies, a phenomenon observed across many different academic fields and known as “Simpson’s Paradox”. The preference of performance metrics which utilise the square of the error, such as root mean squared error (RMSE) and r², over ones which use the absolute error, such as mean absolute error (MAE), makes comparing results between models and studies difficult. Ultimately, comparisons between studies are either difficult or unwise depending on the metrics used, and this literature review recommends that efforts are made to standardise the reporting of calibration and correction studies. By utilising metrics which do not use the square of the error (e.g., MAE), models can be more easily compared within and between studies. By not only reporting the raw error but also the error normalised by multiple factors (including the reference mean and reference absolute deviation), the variabilities induced by environmental factors such as proximity to pollution sources can be minimised. Full article

This study aims to present the benefits of unmanned aircraft systems (UAS) in atmospheric aerosol research, specifically to obtain information on the vertical variability of aerosol single-scattering properties in the lower troposphere. The results discussed in this paper were obtained during the Polish Radar and Lidar Mobile Observation System (POLIMOS) field campaign in 2018 at a wetland and rural site located in the Rzecin (Poland). UAS was equipped with miniaturised devices (low-cost aerosol optical counter, aethalometer AE-51, RS41 radiosonde) to measure aerosol properties (scattering and absorption coefficient) and air thermodynamic parameters. Typical UAS vertical profiles were conducted up to approximately 1000 m agl. During nighttime, UAS measurements show a very shallow inversion surface layer up to about 100–200 m agl, with significant enhancement of aerosol scattering and absorption coefficient. In this case, the Pearson correlation coefficient between aerosol single-scattering properties measured by ground-based equipment and UAS devices significantly decreases with altitude. In such conditions, aerosol properties at 200 m agl are independent of the ground-based observation. On the contrary, the ground observations are better correlated with UAS measurements at higher altitudes during daytime and under well-mixed conditions. During long-range transport of biomass burning from fire in North America, the aerosol absorption coefficient increases with altitude, probably due to entrainment of such particles into the PBL. Full article

With escalating global environmental challenges and worsening air quality, there is an urgent need for enhanced environmental monitoring capabilities. Low-cost sensor networks are emerging as a vital solution, enabling widespread and affordable deployment at fine spatial resolutions. In this context, machine learning for the calibration of low-cost sensors is particularly valuable. However, traditional machine learning models often lack interpretability and generalizability when applied to complex, dynamic environmental data. To address this, we propose a causal feature selection approach based on convergent cross mapping within the machine learning pipeline to build more robustly calibrated sensor networks. This approach is applied in the calibration of a low-cost optical particle counter OPC-N3, effectively reproducing the measurements of ${\mathrm{PM}}_1$ and ${\mathrm{PM}}_{2.5}$ as recorded by research-grade spectrometers. We evaluated the predictive performance and generalizability of these causally optimized models, observing improvements in both while reducing the number of input features, thus adhering to the Occam’s razor principle. For the ${\mathrm{PM}}_1$ calibration model, the proposed feature selection reduced the mean squared error on the test set by 43.2% compared to the model with all input features, while the SHAP value-based selection only achieved a reduction of 29.6%. Similarly, for the ${\mathrm{PM}}_{2.5}$ model, the proposed feature selection led to a 33.2% reduction in the mean squared error, outperforming the 30.2% reduction achieved by the SHAP value-based selection. By integrating sensors with advanced machine learning techniques, this approach advances urban air quality monitoring, fostering a deeper scientific understanding of microenvironments. Beyond the current test cases, this feature selection method holds potential for broader applications in other environmental monitoring applications, contributing to the development of interpretable and robust environmental models. Full article